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   "source": [
    "# 第二次作业第2题\n",
    "作者：赵飞\n",
    "学号：202018019427078\n",
    "日期：2021-04-24"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import leastsq, curve_fit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成待拟合数据\n",
    "def y_gauss(x, a, b, c):\n",
    "    '''\n",
    "    高斯型待拟合函数\n",
    "    '''\n",
    "    return a*np.exp(-(x-b)**2/(2*c**2))\n",
    "\n",
    "a, b, c = 1, 5, 2   # 参数真实值\n",
    "p0 = [0.5, 4, 3]    # 待拟合参数初始值\n",
    "x = np.linspace(0, 10, 200)\n",
    "y0 = y_gauss(x, a, b, c)\n",
    "y1 = y0 + 0.1*np.random.randn(len(x))   # 加入高斯噪声\n",
    "# 1. 使用curve_fit()拟合\n",
    "popt, dummy_ = curve_fit(y_gauss, x, y1, p0=p0)\n",
    "y2 = y_gauss(x, *popt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "真实参数： [1, 5, 2]\ncurve_fit()拟合参数： [0.97927203 4.97384735 2.01557134]\nleastsq()拟合参数： [0.97927203 4.97384735 2.01557134]\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ff3fcecca0>"
      ]
     },
     "metadata": {},
     "execution_count": 31
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 2. 使用leastsq()拟合\n",
    "def residuals(p, y, x):\n",
    "    '''\n",
    "    误差函数\n",
    "    '''\n",
    "    return y-y_gauss(x, *p)\n",
    "\n",
    "plsq = leastsq(residuals, p0, args=(y1, x))\n",
    "y3 = y_gauss(x, *plsq[0])\n",
    "\n",
    "# 展示结果\n",
    "print(\"真实参数：\", [a,b,c])\n",
    "print(\"curve_fit()拟合参数：\", popt)\n",
    "print(\"leastsq()拟合参数：\", plsq[0])\n",
    "# 绘图\n",
    "plt.plot(x, y0, label=\"real data\")\n",
    "plt.plot(x, y1, label=\"real data with noise\")\n",
    "plt.plot(x, y2, '--',label=\"fit by curve_fit()\")\n",
    "plt.plot(x, y2, '--',label=\"fit by leastsq()\")\n",
    "plt.legend()"
   ]
  }
 ]
}